Crisis Management

How can AI improve flash flood forecasting ?

The SPACE Business Unit of CS GROUP brings its technical expertise to design & develop the use case “Improvement of flash flood forecasting thanks to the use of AI”. This use case is about testing the feasibility of modeling flood events by neural network, and to evaluate the sensitivity of the model to the available data.

What are the key stages in developing a reliable AI model ?

For this purpose, we propose the following 4-step approach

Establish a resulting fine-scale hydrodynamic reference model on the selected area with historical episodes.

Step 01

Develop and adjust the AI model with a minimum number of parameters, easily accessible.

Step 02

Qualify the AI model by comparison with the results of the hydrodynamic model, considered as the reference to be reached.

Step 03

Qualify the AI model by testing the impact and sensitivity to input parameters, weights, etc. to avoid over-sampling, and/or to avoid missing some key parameters.

Step 04

Why was Nîmes chosen ?

For its top-notch representativeness of flash flood events in urban areas, the city of Nîmes was chosen. The existence of the ESPADA system, the network of sensors in place, and the richness of historical data makes it an excellent ground for the development of the model. If the results are up to expectations, the methodology could be applied to other urbanized areas, not benefiting from these input data.

What role does the CS METIS platform play in optimising this project ?

It is also expected that the CS METIS platform will be used for the implementation and configuration of the hydrodynamic reference models as well as the integration of the AI models in order to optimize them by reducing the input parameters, the number of announcement types, while taking advantage of the ExtremeXP environment (testing and validation of the use case on the external platform that will be provided by a project partner).